{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "## Predicting Global Supply Chain Outcomes for Essential HIV Medicines using Machine Learning Methods\n",
    "------\n",
    "\n",
    "### Author: Tichakunda Mangono\n",
    "\n",
    "### **Capstone Project, Udacity Machine Learning Engineer Nanodegree, September 2017**\n",
    "\n",
    "\n",
    "\n",
    "- ***Key Question:*** *Can we use procurement transaction data to predict whether a delivery is delayed and estimating the length of the delay*\n",
    "- ***Main Data Source:*** *From The Website: https://data.pepfar.net/additionalData. Procurement transaction data from the Supply Chain Management System (SCMS), administered by the United States Agency for International Development (USAID), provides information on health commodities, prices, and delivery destinations.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 3: Pipeline, Model Benchmark & Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Load/Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Import relevant libraries\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "import my_helper_functions as mhf\n",
    "import pandas_profiling as prof\n",
    "import missingno as msno\n",
    "import pivottablejs as pvt\n",
    "import datetime as dt\n",
    "import os\n",
    "import xgboost as xgb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trying normal method for: ...  0\n",
      "Sucess for: ...  0\n",
      "trying normal method for: ...  1\n",
      "Sucess for: ...  1\n",
      "trying normal method for: ...  2\n",
      "Sucess for: ...  2\n",
      "trying normal method for: ...  3\n",
      "Sucess for: ...  3\n",
      "trying normal method for: ...  4\n",
      "Sucess for: ...  4\n",
      "trying normal method for: ...  5\n",
      "Sucess for: ...  5\n",
      "trying normal method for: ...  6\n",
      "Sucess for: ...  6\n",
      "trying normal method for: ...  7\n",
      "Sucess for: ...  7\n",
      "trying normal method for: ...  8\n",
      "Failed with encoding error, trying again for: ...  8\n",
      "Sucess for: ...  8\n",
      "trying normal method for: ...  9\n",
      "Sucess for: ...  9\n",
      "Dataframes available: dict_keys(['dnum', 'dnum_country', 'dnum_vendor', 'dnum_factory', 'dnum_brand', 'dnum_molecule_test', 'dnum_lpifsi', 'ddate', 'dobject', 'dtrend']) \n",
      " The data shapes: \n",
      "---[(10324, 9), (10324, 12), (10324, 12), (10324, 12), (10324, 12), (10324, 12), (10324, 24), (10324, 10), (10324, 64), (10324, 6)]\n"
     ]
    }
   ],
   "source": [
    "# Load in the data\n",
    "_names =['dnum', 'dnum_country', 'dnum_vendor', 'dnum_factory', 'dnum_brand', 'dnum_molecule_test'\n",
    "         , 'dnum_lpifsi', 'ddate', 'dobject','dtrend']\n",
    "data_dict = mhf.load_clean_data(names_ =_names) \n",
    "print(\"Dataframes available: {} \\n The data shapes: \\n---{}\".format(data_dict.keys()\n",
    "                                                                    ,[d.shape for d in data_dict.values()]))\n",
    "#Assign to named dataframes\n",
    "dnum,dnum_country, dnum_vendor,dnum_factory,dnum_brand,dnum_molecule_test,dnum_lpifsi,ddate, dobject, dtrend = [data_dict[n] for n in _names]\n",
    "dobject = dobject.apply(lambda x: pd.Categorical(x))\n",
    "ddate['delivery_delay_time'] = ddate['delivery_delay_time'].apply(lambda x: pd.to_timedelta(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Use Feature Selection Results \n",
    "#### Supported by Dimensionality Reduction, Feature Importance analysis from previous notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "so =[ #'pq_date_new_yr', 'pq_date_new_mn', 'pq_date_new_dy',\n",
    " 'po_date_new_yr', 'po_date_new_mn', 'po_date_new_dy',\n",
    "  'del_date_scheduled_mn', 'del_date_scheduled_dy'\n",
    " #, 'del_date_client_yr', 'del_date_client_mn','del_date_client_dy'\n",
    " #, 'del_date_recorded_yr', 'del_date_recorded_mn','del_date_recorded_dy'\n",
    " #, 'pq_date_new_wd', 'pq_date_new_wk','pq_date_new_qt'\n",
    " , 'po_date_new_wd', 'po_date_new_wk', 'po_date_new_qt'\n",
    " ,'del_date_scheduled_wd', 'del_date_scheduled_wk','del_date_scheduled_qt'\n",
    " #, 'del_date_client_wd', 'del_date_client_wk','del_date_client_qt'\n",
    " #, 'del_date_recorded_wd', 'del_date_recorded_wk','del_date_recorded_qt'\n",
    " #, 'delayed'\n",
    " #, 'proj_code', 'pq_no', 'po_no','ship_no'\n",
    " #, 'country'\n",
    "    , 'mngr', 'fulfill_via', 'vendor_terms'\n",
    " ,'ship_mode'\n",
    " #, 'pq_date'\n",
    " #, 'po_date'\n",
    "    , 'prod_grp', 'sub_class'\n",
    "    , 'vendor'\n",
    " #,'itm_desc'\n",
    " , 'molecule_test', 'brand', 'dosage'\n",
    " , 'dosage_form','factory'\n",
    "    , 'first_line'\n",
    " #, 'weight', 'freight_cost'\n",
    " ,'del_date_scheduled_yr', 'ship_config', 'weight_captured_seperately'\n",
    " ,'freight_invoiced_seperately', 'freight_in_cmdty_cost'\n",
    " #,'factory_address'\n",
    " , 'origin_country'\n",
    "    , 'origin_continent'\n",
    " #, 'name'\n",
    " , 'dest_country'\n",
    "# , 'orig_origin_country'\n",
    "]\n",
    "\n",
    "snlf = [#'dest_mean', 'dest_sum', 'dest_count'\n",
    "        #, 'orig_mean', 'orig_sum','orig_count',\n",
    "    'dest_fsi','dest_intl_ship', 'dest_timeliness'\n",
    ", 'dest_track_trace', 'dest_customs', 'dest_lpi', 'dest_infra',\n",
    "        'dest_logistic_qlty',\n",
    "    'orig_fsi','dest_count','orig_intl_ship', 'orig_timeliness'\n",
    ", 'orig_track_trace', 'orig_customs', 'orig_lpi', 'orig_infra',\n",
    "        'orig_logistic_qlty']\n",
    "\n",
    "#dnum\n",
    "sn =['ln_itm_qty', 'ln_itm_val', 'pk_price','line_itm_ins', 'ln_itm_weight']\n",
    "# dnum_country\n",
    "snc =  ['country_qtycount', #'country_qtymean',\n",
    "        'country_valmean',# 'country_insmean',\n",
    "       'country_weightmean'#,  'country_costmean'\n",
    "       ]\n",
    "# dnum_vendor\n",
    "snv=[#'vendor', 'vendor_qtycount',\n",
    "     'vendor_qtysum',\n",
    "       'vendor_qtymean'\n",
    "    , 'vendor_valsum', 'vendor_valmean', 'vendor_inssum',\n",
    "       'vendor_insmean', 'vendor_weightsum',  'vendor_costsum'\n",
    "]\n",
    "# dnum_factory\n",
    "snf=[#'factory',\n",
    "     'factory_qtycount', \n",
    "     'factory_qtymean'#, 'factory_valmean','factory_costmean'\n",
    "    ]\n",
    "#dnum_brand\n",
    "snb = [#'brand',  'brand_qtymean',      \n",
    "       'brand_valmean'\n",
    "#       ,'brand_insmean', 'brand_weightsum', 'brand_weightmean', 'brand_costmean'\n",
    "]\n",
    "#dnum_molecule_test\n",
    "snm=[#'molecule_test', \n",
    "     'molecule_test_qtycount'\n",
    "     #,'molecule_test_qtymean', 'molecule_test_valmean'\n",
    "     ,'molecule_test_insmean']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 1. Model Pipeline\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---\n",
      " (10324, 725) Index(['del_date_scheduled', 'del_date_client', 'del_date_recorded', 'pq_date',\n",
      "       'po_date', 'po_date_new', 'pq_date_new', 'anticipated_lead_time',\n",
      "       'actual_lead_time', 'delivery_delay_time'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Use Feature Selection columns\n",
    "dn,dnc, dnv = dnum[sn],dnum_country[snc], dnum_vendor[snv] \n",
    "dnf, dnb, dnm = dnum_factory[snf],dnum_brand[snb],dnum_molecule_test[snm]\n",
    "dnlf = dnum_lpifsi[snlf]\n",
    "do =  dobject[so]\n",
    "# #ddate #dt #do\n",
    "\n",
    "# Import pipeline and preprocessing modules \n",
    "from sklearn.preprocessing import FunctionTransformer,PolynomialFeatures, normalize\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, OneHotEncoder, LabelBinarizer ,StandardScaler\n",
    "from sklearn.pipeline import TransformerMixin, _BaseComposition \n",
    "from sklearn.pipeline import FeatureUnion, make_pipeline, Pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "log1p = FunctionTransformer(np.log1p)\n",
    "make_num_pipe = make_pipeline(log1p, StandardScaler()) \n",
    "make_num_pipe_2 = make_pipeline(StandardScaler()) \n",
    "make_cat_pipe = make_pipeline(mhf.Dummifier())\n",
    "make_lab_pipe = make_pipeline(mhf.Labeler())\n",
    "\n",
    "# Drop redundant columns\n",
    "entities =['country', 'factory', 'vendor', 'brand', 'molecule_test']\n",
    "for d in [dnc, dnf, dnv, dnb, dnm]:\n",
    "    for c in entities:\n",
    "        try:\n",
    "            d.drop(c, axis=1, inplace=True)\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "# Selective data        \n",
    "dn,dnc, dnf, dnv, dnb, dnm = [pd.DataFrame(make_num_pipe.fit_transform(d), index=d.index\n",
    "                            , columns=d.columns) for d in [dn,dnc, dnf, dnv, dnb, dnm]]\n",
    "dnlf = pd.DataFrame(make_num_pipe_2.fit_transform(dnlf), index=dnlf.index,columns=dnlf.columns) \n",
    "dtr = pd.DataFrame(make_num_pipe_2.fit_transform(dtrend), index=dtrend.index,columns=dtrend.columns)\n",
    "do = make_cat_pipe.fit_transform(do)\n",
    "\n",
    "# Delayed\n",
    "delayed=pd.DataFrame(make_lab_pipe.fit_transform(dobject.delayed.map({True:1, False:0})), columns=['delayed'])\n",
    "try:\n",
    "    do.drop('delayed', axis=1, inplace=True)\n",
    "    do_alt.drop('delayed', axis=1, inplace=True)\n",
    "except:\n",
    "    pass\n",
    "\n",
    "data = pd.concat([do,dnlf,dn,dnc, dnv, dnf, dnb, dnm,dtr], axis=1)\n",
    "\n",
    "print(\"\\n---\\n\",data.shape,ddate.columns)\n",
    "#print(\"\\n---\\n\",data_alt.shape,ddate.columns)\n",
    "\n",
    "# Remember the ddate dataframe will be needed later for regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 2. Model Benchmarks\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import all the modules for Modeling and Prediction\n",
    "# Import sklearn and models\n",
    "from sklearn.ensemble import  BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier, BaggingRegressor, ExtraTreesRegressor, RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.svm import LinearSVC, SVC, LinearSVR, SVR\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegressionCV, LogisticRegression, SGDClassifier, SGDRegressor\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB    \n",
    "from sklearn.neural_network import MLPClassifier, MLPRegressor \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection, metrics\n",
    "import yellowbrick as yb\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Benchmark\n",
    "#### *Random Forest Classifier with Default parameter*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XTrain, yTrain shapes BEFORE Oversampling: (6710, 725) and (6710, 1)\n",
      "XTrain, yTrain shapes AFTER Oversampling: (6710, 725) and (6710, 1)\n",
      "Shape of XTrain: (6710, 725) yTrain: (3614, 725) XTest: (6710, 1) yTest: (3614, 1)\n",
      "\n",
      "----\n",
      "\n",
      "Total Importance of 30 features: importance    0.51319\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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55pvMnz8flUr12JsSCyGEEKJYtU7wFqWTBG/DkJ0lw5A665/U2DCkzvojCd7P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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf9f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Partition the data into predictors and label\n",
    "X_clf= data\n",
    "y_clf = delayed\n",
    "# Train test split custom function with OverSampling to balance datasets if required\n",
    "X_tr_clf, X_ts_clf, y_tr_clf, y_ts_clf = mhf.train_test_oversample(X_clf\n",
    "                                                                                   , y_clf, test_size=0.35, use_smote=False)\n",
    "print(\"\\n----\\n\")\n",
    "# Fit the regression to the data and train\n",
    "clf = RandomForestClassifier(random_state=121)\n",
    "clf.fit(X_tr_clf, y_tr_clf)\n",
    "d_feat_imp_clf = mhf.plot_feature_importances(clf, X_tr_clf, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KBRERMSkURETEpFAQERGTQkFEREwKBRERMWXr13GKiEhmRR7zwPVyUpbWTfb0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc8737f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'f1': {'delayed': 0.45890410958904115, 'on-time': 0.95243829018663473},\n",
       " 'precision': {'delayed': 0.74860335195530725, 'on-time': 0.92110625909752553},\n",
       " 'recall': {'delayed': 0.33086419753086421, 'on-time': 0.98597693985665313}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict and plot F1-Scores, Precision and Recall\n",
    "plt.subplots(figsize=(6,5))\n",
    "y_pred_clf = clf.predict(X_ts_clf)\n",
    "mhf.visual_model_selection(X_tr_clf, X_ts_clf, y_tr_clf, y_ts_clf, clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min  25%  50%  75%  max\n",
      "delayed  3614.0  0.112064  0.315489  0.0  0.0  0.0  0.0  1.0\n",
      "pred     3614.0  0.049530  0.217001  0.0  0.0  0.0  0.0  1.0\n",
      "delayed    134\n",
      "pred       134\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Make dataframe of delayed vs. predicted\n",
    "df_pred=pd.DataFrame(y_ts_clf, columns=['delayed']); df_pred['pred']= y_pred_clf\n",
    "print(df_pred.describe().T)\n",
    "# Make DataFrame of True Positives for regression prediction (length/extent of delay) \n",
    "df_true_pos = df_pred[(df_pred.delayed==1) & (df_pred.delayed==df_pred.pred)]\n",
    "print(df_true_pos.sum())\n",
    "# Save to disk\n",
    "path = os.curdir+\"\\Data\\Results\\\\\"\n",
    "df_pred.to_csv(path+'classifier_benchmark_predicted.csv')\n",
    "df_true_pos.to_csv(path+'classifier_benchmark_true_positives.csv') # Save to data folders\n",
    "# Plot to visualize the prediction\n",
    "#plt.subplots(figsize=(15,2))\n",
    "#plt.scatter(df_pred.index, df_pred.iloc[:,0], color='b')\n",
    "#plt.scatter(df_pred.index, df_pred.iloc[:,1], color='r')\n",
    "#plt.title(\"Plotting Predictions (Red) over Actuals (Blue)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspect the False Positive and False Negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get a sense of what the model is missing\n",
    "# Go back to feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression Model Benchmark\n",
    "#### *Random Forest Regressor with Default parameter*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now getting train test splits for regression...\n",
      "Shapes:\n",
      " (1052, 725)\n",
      ",(134, 725)\n",
      ",(1052,)\n",
      ",(134,)\n"
     ]
    }
   ],
   "source": [
    "# Here, for regression, we training only on the positive samples? \n",
    "# Conditional probability question changes things. BAYES!\n",
    "X_tr_reg, X_ts_reg, y_tr_reg, y_ts_reg = mhf.train_test_conditional(X_clf, y_clf, df_true_pos, ddate, delayed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Importance of 30 features: importance    0.810062\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZwuLFi6lTpw4A0dHRpeZYtWpVqeuvLNItv4jUQRmpU/mkRspInZSROinzXCaa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\naCgUEREREeAf9gwftRy2o6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1099f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the regression, fit and plot\n",
    "reg = RandomForestRegressor(random_state=121)\n",
    "reg.fit(X_tr_reg, y_tr_reg)\n",
    "d_feat_imp_reg = mhf.plot_feature_importances(reg, X_tr_reg, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared:  0.858374718039 RMSE : 12.9585072616 Target Variable Mean:  25.955223880597014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10af2ef0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b25d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicts the test values\n",
    "y_pred_reg = reg.predict(X_ts_reg)\n",
    "# Accuracy and error calcs\n",
    "r2, rmse = metrics.r2_score(y_ts_reg, y_pred_reg), np.sqrt(metrics.mean_squared_error(y_ts_reg, y_pred_reg))\n",
    "print(\"R-Squared: \", r2, \"RMSE :\", rmse, \"Target Variable Mean: \", y_ts_reg.mean())\n",
    "# Print out the test and pred side by side\n",
    "df_pred_reg =pd.DataFrame(y_ts_reg)\n",
    "df_pred_reg['pred']= y_pred_reg\n",
    "df_pred_reg.plot(kind=\"barh\", figsize=(20,30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark Model Findings\n",
    "\n",
    "- **RandomForest Classifier**\n",
    "    - F1-score of ~44%, Recall of 33%, and Precision of 71%.\n",
    "    - The top 30 features explain almost 80% of the model.\n",
    "    - More feature ideas - polynomials, combinations of features, add the time series aspect instead of features from time components?\n",
    "    \n",
    "    \n",
    "- **RandomForest Regressor**\n",
    "    - Does decently well, at R2 of 80%\n",
    "    - RMSE of 16 days compared to a mean of 27 days among the delayed items "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 3.  Model Selection\n",
    "---\n",
    "Try out several models and pick the most promising ones to fine-tune into the final model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XTrain, yTrain shapes BEFORE Oversampling: (6710, 725) and (6710, 1)\n",
      "XTrain, yTrain shapes AFTER Oversampling: (6710, 725) and (6710, 1)\n",
      "Shape of XTrain: (6710, 725) yTrain: (3614, 725) XTest: (6710, 1) yTest: (3614, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d81d68>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fnx80Gg2srKzg5+eHxMREDBgwABUrVsS7776Ld999F3PnzgUAVKxYEf7+/vmmofxvmS3h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\nGGOMEeEizBhjjBHhIswYY4wR+T+HmGUzDut36gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0af28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train test split custom function with OverSampling to balance datasets if required\n",
    "X_tr_clf_sel, X_ts_clf_sel, y_tr_clf_sel, y_ts_clf_sel = mhf.train_test_oversample(X_clf\n",
    "                                                                                   , y_clf, test_size=0.35, use_smote=False)\n",
    "# List of classifiers\n",
    "clfs = [LinearSVC, SVC, KNeighborsClassifier, LogisticRegressionCV, LogisticRegression, SGDClassifier\n",
    ",BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier, MLPClassifier\n",
    "       ,GaussianNB, LinearDiscriminantAnalysis]\n",
    "# List of classiffier names\n",
    "cn = ['LinearSVC' , 'SVC', 'KNeighborsClassifier', 'LogisticRegressionCV', 'LogisticRegression', 'SGDClassifier'\n",
    ",'BaggingClassifier', 'ExtraTreesClassifier', 'RandomForestClassifier', 'MLPClassifier'\n",
    "      , 'GaussianNB', 'LinearDiscriminantAnalysis']   \n",
    "# Plot the f1 scores\n",
    "acc = []\n",
    "for clf in clfs:\n",
    "    try:\n",
    "        acc.append(mhf.model_selection(X_tr_clf_sel, X_ts_clf_sel, y_tr_clf_sel, y_ts_clf_sel\n",
    "                                       , clf(), 'clf'))\n",
    "    except:\n",
    "        acc.append(0)\n",
    "d_acc = pd.DataFrame(acc, cn)\n",
    "d_acc.columns = [\"f1score\"]\n",
    "d_acc.sort_values(by ='f1score',ascending=False).plot(kind=\"bar\", title=\"F1 Score Comparison for Classifiers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j/PHHHzRo0ICmTZtmd20iIpLDsnRO4ZFHHuGRRx4B4LvvvuPixYv4+Phka2Ei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\nWSi67wAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c62f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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krqEwbdo0Zs2axcsvv2xMGzp0KMHBwYSEhGR7cSIikrPuevro+vXr1KhR45bp\nderUYebMmdlWlIjI48qudD7szJastbXP+TMydx0pZGRkYLHcWrzFYtGzj0REHkN3DYXq1aszb968\nW6YvWLCAihUrZltRIiJiG3c9fTRs2DD69u1LWFgYzz77LFarld9++w1vb28WLlyYUzWKiEgOuWso\nuLm5sWLFCnbt2sXvv/+OyWSiW7duPP/88zlVn4iI5KB7fqPZzs6OmjVrUrNmzZyoR0REbChLzz4S\nEZEng0JBREQMCgURkVzMYrEwbtw4AgMD6dGjB5GRkbdtN3bs2Cx9v0yhICKSi23evJm0tDRWr15N\nUFDQbZ82sWrVKo4fP56l/hQKIiK52L59+6hTpw4Azz33HIcPH840f//+/Rw4cIDAwMAs9adQEBHJ\nxRISEnBzczM+29vbk5GRAUB0dDTz589n3LhxWe4vSy/ZERGRR5ObmxuJiYnGZ4vFgoPDjUP7t99+\nS1xcHH379iUmJoaUlBR8fX1p167dHftTKIiI5GJVq1Zl69atNG/enIiICPz9/Y15PXv2pGfPngCE\nhobyxx9/3DUQQKEgIpKrNW7cmB07dtC5c2esVivBwcGEhYWRlJSU5esIf6VQEBHJxUwmE5MmTco0\nzc/P75Z29xohGP09lKpEROSxoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDQkFERAwKBRERMSgU\nRETEoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDHp0tIpKTfJ8GU1LW2lpcIPHezR4mjRRERMSg\nUBAREYNCQUREDAoFERExKBRERMSgUBAREYNCQUREDAoFERExKBRERMSgUBAREYNCQUREDAoFEREx\nKBRERMSgUBAREYNCQUREDAoFEREx6CU7NuTcsAH5Ro3CLo8T6b//TlzQ21gTEoz5Lh3a49b3deOz\nyd2dp0qU4EKBAlguXwbAvlhRCn31FZcaN8ESF5epf5fAQPIGNOXKa/+XMxv0CPrmx1O8M2sbqWkZ\nVCpbiI+Dm5HPLU+mNp+vP8LMT/ZgZ2eHS14HBgytSNUa0HHQOk5GXjXanT53lbrVS7B+UXu27opk\n+LStZGRYye/pzOwxDalcrlBOb94j4Zu9l3hn+VFSMyxUKpmPjwdVIp+L423brtt1kVdnR3BtdTMA\nzGYrwz49wnf7Y8gwWwlq60f/gJKZlvn0+7Os23WRr8a+kO3bIhop2IzJ2xuv92YR27cvl16uhzny\nLB7vjM7oIk6+AAAcmElEQVTUJmnNWqKbNLvxX/NXMMfEcPbsWSMQXDq0p2DoWuyLFsm0nJ2nJ54h\nwXhOngR2djm2TY+amNgk/m/0RtbMbc3RTa9TuoQHo2b+lKnNsT+uMGLGj2z8uCO/rn+NMQNq8vbb\nbwPw5Qdt+HX9a/y6/jUW/6spnvmcmTe+MdfiU2k/cB3TR9TnQFgvFkxoQuDg9aSmZdhiM20q5loq\n//fBAdaMrsbRhfUpXcSFUcuO3rbtiT8TePvT37BYrca0DzdFcvLPRA7Nq8ue915izld/sOf4jV9u\nYuPT6L/gIIMWH+Evi0g2y5FQSE1NpUGDBredt3v3boYOHfrQ1zlz5kxCQ0Mfer8PS566L5N24AAZ\np88AkLD8M1zatrlje/c338By+QqX/xsIpsKFydu0KZd7vHpLW5eWr2C+FM21f03Oltpzi+9+Pk31\nZ4tQppQ3AAO6VOHfYb9h/csRJo+TAx9NbkbRQm4APF+xCFeuXCEtzWy0SUsz89qoDcx+pwEliubj\nxJlYPNzz0LDmjd9oy/nlJ59bHnb++mcObt2j4btfY6hexpMyxW7svwEBJfn3T+cz7WOApFQzPd6L\nYFbv8pmmr9t5kdcalsDB3oSXmxOBdYrx+Y/nAfji5wsU9XJmRq9ncmZjBNBIwWYcihXD/Of/DiLm\nCxcw5cuHnZvbLW1NXl64932dq+MnGNMsly5x5fW+ZJw4cUv7xM8+J372+1hTUrKl9twi6mI8xYu4\nG5+LF3HnekIa8YlpxrRSxT1oUc8PAKvVStDUrbz88ss4OdkbbT5Zc5Bihdxo29gfAP/S3iQkpvPd\nz6cB2HvwAkdOXuZCzP9O/T0poi6nULyAs/G5eAFnridlEJ+cedTUf/5B+jb1oVKpfH9bPpkSBfL+\nZfm8nL984+9t/4CSjO/iT9489kjOybZrComJiQwfPpzr16/j4+MDwLFjx5g8+cZvr56engQHB2da\n5vPPP+e7774jOTkZLy8v5s2bx+jRo2nZsiX16tXj1KlTTJs2jfnz5zN+/HgiIyOxWCwMGTKEGjVq\nsGnTJhYuXIi3tzfp6en4+vpm1+Y9ONMd8thsvmWSa/duJH/3PeaoqGwu6vFisdz+nIO96dZTaolJ\nafQatZGoi9cJeX8+sNGY9/6ycD6c1NT4nM8tD+sWtOXd97czYvqP1KleggYv+uDk+OQdvLKyjxds\nOIODvR3/19iHM5cyv7D+dovf7s9Hck62hcKqVavw9/dn6NChHDhwgN27dzN27FiCg4N5+umn+fLL\nL/n444+pVasWABaLhatXr7J06VJMJhO9e/fm0KFDdOzYkZUrV1KvXj3WrFlDhw4d+PLLL/Hy8iI4\nOJi4uDi6d+/OunXrCAkJITQ0FE9PT/r27ZulOi9/vDi7dsFdpXt74+XlxaXyN4bTTk5OFMnI4OLq\nlbe09X7mGaKiokj4ah0Al/77/5uKA9GfL8f8t0DJnz8/Jk/PW9rnpEvxNls1Fi83ju7bzP74TgBc\nuHCBfPkWc8zcHf5S18WLFxk6dCilSpVi1vzxODs7G8scPXqUpHQX3J8JYn/8jYOVxWIhipPMWjDM\n6KNDhw6kFwhkf7x/zm3gXxWzzWot/hs4enYz+4tNAG7u45855vu/U5cLtvUkJcWJcsOPk56eTnKa\nlXLDjzNnTgxexSuww64NTsXqAxCethjHkvHsLxZkLB/pGcY15y3GOiR7ZVsonDlzhrp16wJQuXJl\nHBwcOHXqFBMnTgQgPT2dUqVKGe1NJhOOjo4MGzYMFxcXLl68SEZGBjVq1GDy5MnExsayY8cOhg0b\nxpQpU9i3bx8HDx4EICMjg5iYGDw8PPDy8gKgSpUqWaqzQJ++2EdHP8QtzxpT/vy4b/mepwYPIeP0\nGfKNGknqb79TeFhQpnZ2Hh44h+/BtUkzXDMyuPTVOgq3+tu1h/NRFOre89a7jzp1JE+L5hR+tVd2\nb84dPbW1q83WXbxRIvPmhON+ZRFlSnkzevFPtGvkQ1X3L4w2sVeT6dBvOa+2q8j4t6oAX7E/vpPR\nZtuRcJrW9KZavi+NZaxWK22GLGTdgrY8/2xRvtx4FHenRAKr/oqdXUROb+YNx36zyWqLl0pl3oFd\nuIcPp0wxN0Yv+5121fNR9c8JRpvDIf8bsZ+5lMSzA89ydKY/+wsWpNtziXz/xQwGP72VhGQz2zbs\nYOEbz2Za/uDVKDxSLmSaltNS7T05UniIzdafk7ItFPz8/IiIiKBRo0b89ttvZGRkULp0aaZNm0ax\nYsXYt28fMTExRvujR4+yefNmvvzyS5KTk2nXrh1WqxU7OztatWrF5MmTqV27No6Ojvj6+lKkSBH6\n9+9PSkoKCxcupECBAly/fp3Y2Fi8vb05dOgQRYoUuUuFtmW5coW4YUF4L/4QO0dHMiIjiR08FMdK\nlfCaOZ3oJjdu2XMoVQrLpWjIePLubHlQhfK78unUADoOWk9auhk/H0+WTWtB+KELvP7uJn5d/xoL\nV0Zw9sJ11n1/gnXf37g+k2xZx47lTcnvlZcTkXGUesojU792dnasmPUKfcduIi3dTNGCbvxnQVvs\nnsA7vQp55uHTwZXpGLKPtAwrfkVcWDb0OcJPXOX1eQf5dc7Ld11+QEBJTl1I4rlB20jLsNC3WUnq\nVsyfQ9XL7dhZ/36bwEOSmprKiBEjiI6OxtfXl/DwcGbNmsW0adPIyMjAzs6OKVOmEB0dzapVqwgO\nDqZfv36kpd24COjk5ESHDh1o1aoVly9fpl69eqxfvx4/Pz/S0tJ49913+fPPP0lISKBr16506tSJ\nH3/8kTlz5uDh4YGDgwPNmzenXbt2d6zv8OHDNhsp3K/bjhQeYbYcKdyvv44Ucg0bjRQexP5iE2z6\n2/8/cXOkULFiRfLkyXPvBW7Xx3+PORVcvyaPKeneCwCpFheOJL7yQOv9p7JtpJAnTx7mzJlzy/TP\nPvss0+fSpUtTo0YNAJYvX37bvsxmM9WqVcPP78ZdIk5OTkyfPv2WdvXq1aNevXoPWLmIyJPrkb8l\n9bvvvqNPnz4MGjTI1qWIiDz2HvnHXDRp0oQmTZrYugwRkSfCIz9SEBGRnPPIjxRERB4ndl5lsHNM\nz1rbdEdIzOaC/kYjBRERMWikICKSi1ksFiZMmMCxY8dwcnJi8uTJlCz5v8ePf/311yxbtgx7e3v8\n/f2ZMGECpjs9ZgeNFEREcrXNmzeTlpbG6tWrCQoKIiQkxJiXkpLC+++/z/Lly1m1ahUJCQls3br1\nrv0pFEREcrF9+/ZRp04dAJ577jkOHz5szHNycmLVqlXkzXvjSbQZGRn3/BKcQkFEJBdLSEjA7S+P\n3Le3tyfjv4/FMZlMFChQALjxxeGkpCRq16591/50TUFEJBdzc3MjMfF/tyhZLBYcHBwyfZ4xYwan\nT59m7ty593xGl0YKIiK5WNWqVdm2bRsAERER+Ptnfnz7uHHjSE1NZcGCBcZppLvRSEFEJBdr3Lgx\nO3bsoHPnzlitVoKDgwkLCyMpKYmKFSuyZs0ann/+eV599care3v27Enjxo3v2J9CQUQkFzOZTEya\nNCnTtJsPD4UbryX4R/09lKpEROSxoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDQkFERAwKBRER\nMSgURETEoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDQkFE\nRAwKBRERMTjYugARkSeKZ1nIk8W2qcC57CzmVhopiIiIQaEgIiIGhYKIiBgUCiIiYlAoiIiIQaEg\nIiIGhYKIiBgUCiIiYlAoiIiIQaEgIiIGhYKIiBgUCiIiYlAoiIiIQaEgIiIGhYKIiBgUCiIiYlAo\niIiIQaEgIiIGhYKIiBgUCiIiYlAoiIiIQaEgIiIGhYKIiBgUCiIiYlAoiIiIQaEgIiIGhYKISC5m\nsVgYN24cgYGB9OjRg8jIyEzzf/jhB9q3b09gYCBffPHFPftTKIiI5GKbN28mLS2N1atXExQUREhI\niDEvPT2dqVOn8umnn/LZZ5+xevVqLl++fNf+HLK74EeV1WoFwJw/v40r+efMhQrZuoQsS7W42LqE\n+5Lr6rb3tHUF9yU1l9SdZsoH/O+48SDS0x9u23379lGnTh0AnnvuOQ4fPmzMO3XqFD4+Pnh4eABQ\nrVo19u7dS0BAwB37e2JDIf2/eztu2lQbV/LPXf54sa1LyLLLibau4P4cSXzF1iX8M4VtXcD9OVJ4\niK1L+EfS09Nxdna+r2Xt7e2xt7fn2DHzfS13JwkJCbi5uWVqn5GRgYODAwkJCbi7uxvzXF1dSUhI\nuOv6nthQcHV1xd/fH0dHR+zs7Gxdjog8wqxWK+np6bi6ut53Hw4ODlSsWBGz+Z+HgoPDnQ/Vbm5u\nJCb+77cvi8VitP/7vMTExEwhcds6/1F1jxGTyXTPnSMictP9jhD+ysHB4a4H+PtRtWpVtm7dSvPm\nzYmIiMDf39+Y5+fnR2RkJFevXsXFxYXw8HB69+591/7srA/jJJmIiNiExWJhwoQJHD9+HKvVSnBw\nML/99htJSUkEBgbyww8/MH/+fKxWK+3bt6dbt2537U+hICIiBt2SKiIiBoWCiIgYFAoiImJQKIjc\nw8mTJ8nIyLB1GSI5QqHwGPqn90HLnW3cuJGFCxdy5MgRBUMO+fu9LxaLxUaVPJkUCo8Zi8WCvb09\nVquVAwcOcPHiRVuXlCtZrVZmz55Nw4YNqVSpEuvXr1cw5ACLxYKdnR3x8fFcuHCBhIQETCYdpnKS\n9vZjxGw2YzKZsFqtDBo0iJCQED766CM2b95s69JyHTs7O44dO0ZQUBBdunTBx8eHdevWKRiymclk\n4tKlS/Tu3ZuVK1fSoUMHTp48CTyc5w7JvSkUHiM3RwiffPIJ1apVY+nSpZQtW5aIiAg2bdpk6/Jy\njZvPxVq0aBHu7u4MGjSIrl27UrJkScLCwoiIiNApuofs5imitLQ0pk6dyhtvvEGfPn0wm82sWrWK\nxMREPY4mhygUHgN/PecaHh7O6tWryZMnD3ny5KFhw4YULVqUffv2ceXKFRtWmTtYLBYcHR2JjY3l\n/PnzBAcHU7JkSQYOHEjXrl0pWLAgW7Zs0WjhIbJYLJhMJuLi4jCZTDzzzDOcOHGCQYMGsWTJEkqX\nLk14eLity3xiPLHPPnpcmM1mY4Rw7Ngx/P39GTlyJP/+978pU6YMzz//PAEBAaSmppI/Fz4mPCdZ\nrVZMJhPR0dH079+fp59+moyMDN577z1CQkJ47bXXWLp0KUlJSeTJk8fW5T42bgbCyJEjadeuHc7O\nzoSFhREQEEB0dDSrV69mwYIFti7ziWE/YcKECbYuQu6fyWTCYrHQr18/jh07xty5c6lbty4+Pj58\n+OGHFC9eHH9/fz38LwtuXuAcOXIkvXv3plGjRqxfv55Dhw4xZswYoqKi8PPzo2DBgrYu9bGSlpbG\nqFGjcHNz44033qBkyZIkJyeTlpbGt99+y6RJkyhVqpSty3xi6NlHuVRUVBRFixbFwcGB4OBg7O3t\nGTlyJHv37mXq1KnMmDGD33//nRIlSlC5cmVbl/tIuznaghvPpt+wYQOlS5cmNDSUOnXqsHTpUnx8\nfJg5c6aNK3183Nzn6enpODo6smfPHubPn0/37t1p3LixMT0+Pl6/0OQwnT7KhXbu3ElCQgIlSpQA\noFixYjg6OgJQvXp1GjRowKFDh2jTpo0ty8wVbt7Ce+nSJbZu3UrJkiXx9/fn559/pn379lgsFvz9\n/enbt6+tS31s3NznFy9eZMaMGSQlJdGmTRtat27Nv//9b9LT02natClAppfHSM7QheZcqGbNmjRu\n3Jhly5axY8cOXFxciI6OZvPmzezZs4fvvvsOHx8fW5eZK5hMJmJiYhgxYgSRkZG4urry3HPPkZiY\nSFhYGOPGjaNXr17anw/RzWsIY8aMoV69egwYMICwsDCcnZ3p3bs3X331FSkpKQC648gGNFLIRf56\nmgMgKSmJn3/+mdq1axMXF8ehQ4f4/fffGTlyJFWrVrVhpbnDzbteQkNDKV++PCNHjgRgz549uLm5\nUadOHfr27ctTTz1l40ofDzf3N8D58+fJmzcvLVu2BGDAgAGEhITw2WefUbVqVVxcctk7sh8jCoVc\n4uY51psv1ChfvjwDBgxg6dKl7N27lxo1alCrVi2uX79Ovnz5bF3uI+3mwenm5TQfHx9MJhNJSUm4\nuLiwf/9+ihUrRq1atWxc6ePj5j6PjY0lLi4Os9lMiRIl2LlzJzVr1iQ2NhZXV1eSk5MVCDamC825\nzJtvvkn16tXx9fUlJSWF0qVL88033xAdHc2oUaNwd3fXkPsubh6coqOjWbFiBWXLliU5OZmNGzfy\n0ksvkZSUxK5du5gwYQK+vr62LvexcvNW35deeolKlSpx9uxZrl69SlRUFHFxcbzzzjuZXiUptqFQ\neMStW7eO5ORkunTpwsWLFxk1ahQTJkwgJCSEEiVKEB0dzfTp07ly5QrFihWzdbmPtL/+ttqrVy96\n9erFDz/8QMmSJSlTpgwWi4Vz587RokULSpcubetyHxs3X3r/zjvvULlyZXr06AHARx99RJkyZfD0\n9KRw4cIULVrUxpUK6PTRI+/mHUTvvfcew4YNo1KlSuzcuZN+/fpRpkwZBgwYQGxsrALhHm4GwtWr\nVzl27BgdOnSgTZs2rF69msqVK1OwYEFq1qxp6zIfKzevgdnZ2eHk5ESpUqXw8vIy/iwSEhLw8fHR\niOwRo7uPHlF/f7bOiRMneOONN4xg2Lp1Kz179qRPnz76DSsLbt7x0rdvX44cOcLixYtp2bIlCxYs\nwNfXlxUrVpCYmKiHrj0kNwPh0qVLfPTRR8TGxuLt7c2RI0f4/vvvWb9+Pdu3b9f1g0eQQuERdPM+\nbovFwqRJk5g3bx5z5szBxcWFvn37UqFCBZo1a8aUKVOoW7eurcvNFSwWC99++y0ZGRk0atSIQYMG\nkZiYyObNm5kzZw5DhgzB1dVV12MeEnt7e+M6l9Vq5fjx4zRp0gRXV1eioqLYsmULs2bNokiRIrYu\nVf5G1xQeYQMGDKBMmTLUqVOH6tWrk5KSwsiRI4mNjeWzzz6zdXm5TmxsLF988QVXr16ldevWAJw5\nc4YKFSroewjZYPHixURHR9OzZ0/Gjh1L9erVKVy4MB07diQ5OZm8efPaukS5DY0UHiF/zecLFy5g\nZ2fHsGHDqF69OidOnGDcuHHMmTOHd99914ZV5l7e3t506tSJIkWKsHLlSpydnQkICFAgZBNvb2/c\n3d2ZN28eY8aMwcHBgT///BMAZ2dnG1cnd6JQeESYzeZMpy4KFSqEm5sb7733HnDjH9H169e5evUq\nZcuWtVWZuZ63tzctW7akTJky+j7HQ3bzEe43///iiy/Sq1cvWrZsyYkTJ9i9ezetWrUC9E3lR5lO\nHz0CrFYrdnZ2WCwWBg0ahK+vLydPnqRv376sWrWK69evExMTw4ABA2jQoIGty30s/P3b4XL/lixZ\nQrt27fDw8DD2a3h4OGvWrKF///789ttvnDx5koCAAMqUKWPrcuUedEuqjd0MBIARI0ZQs2ZNAgIC\n6NixI1u2bGHixImcOHECNzc3PT74IVIgPBwJCQl89913REdH069fPzw9PYmJiWHu3Lm89tprlCpV\nilKlSmX6ey6PNo0UbOivz4IBmDdvHs8//zyff/457dq1IzY2looVK1KuXDkbVilyK4vFwpIlS/D1\n9WXu3LnUrl2bq1evEhQUhKenJ3/++SfFihVTGORCuqZgIzff8mW1WnnnnXdYt24dsbGxTJs2jZo1\na1KhQgWWL1+u++blkTRnzhx+/fVXqlSpQufOnenevTve3t689957XL16lWLFimGxWBQIuZBCwQb+\nelF5+PDhmEwm2rRpQ7du3ShRogQJCQkEBQURFBTEM888Y+NqRW7VunVrzp49y6hRo6hQoQKFCxem\nbdu2FCxYkMmTJ3Pt2rVMo2DJPXRNIYf99Ytpe/bs4fz582RkZBAZGYmfnx+jR4/G2dmZRo0a4efn\nZ+tyRW6rZMmSODg4cPLkSWJjY41pzZs35/vvvyctLc3GFcr90jWFHHTz/KrVamXAgAEUKlSI6Oho\nfvzxRypWrMjs2bONt6mJPOpiY2OJiopi6tSp9OrVy3hb2s3HvEvupFCwgXnz5nH+/HmmTp2K1Wpl\nyJAhbNu2jaeeeoo1a9boiz2Sq2zbto2QkBCCgoJo2LChrcuRB6TTRzksPj6e1NRUrly5wtGjRylX\nrhwtW7akWbNmVKxYUYEguc7LL7+Mo6OjRrmPCY0UbODatWuEhoYSFRWFn58f69evZ/DgwdSuXdvW\npYnIE063B9iAh4cHrVu3xtvbmw0bNtCxY0dq166t209FxOZ0+shGvL296d69Oy4uLhw7dsw4lSQi\nYksaKdiQp6cnrVu3plSpUhQoUMDW5YiI6JrCo0APZxORR4VCQUREDDp9JCIiBoWCiIgYFAqSK507\nd46yZcsybty4TNN///13ypYtS2hoqI0qE8ndFAqSa3l6erJ9+3bMZrMxbcOGDXh7e9uwKpHcTaEg\nuZarqyvPPPMMe/fuNabt2LGDWrVqATeeydOhQwfatGnDW2+9RVxcHAAbN26kU6dOtGrViqZNmxrL\nL1myhFatWtGmTRtjBBIaGsqoUaOM/nv06MHu3bvZvXs3HTp0oF27dowcOZLExERGjhxJu3btaN26\nNV9//TUAR48epVOnTrRr144uXbpw5syZnNg1IvdNX16TXC0gIIBNmzbx4osvcvDgQcqWLYvVaiU2\nNpZly5axfPlyPDw8WLVqFTNnzuRf//oXq1atYtGiRXh7e7NmzRo++eQTqlSpwocffsj27duxt7dn\n4sSJXLp06a7rPnPmDFu3bsXd3Z2ZM2dSoUIFpk2bRkJCAp07d6Zy5cosW7aMXr16ERAQwIYNG4iI\niNBrVeWRplCQXK1+/fq8//77WCwWNm7caBx8nZ2duXDhAj179gRuvMfCw8MDk8nE/Pnz+eGHHzh9\n+jR79uzBZDLh4OBAlSpV6NChAw0bNqRbt24ULlz4rusuXbo07u7uAPzyyy+kpKSwdu1aAJKSkjhx\n4gR169Zl0qRJbN++nfr16xuPlxZ5VCkUJFdzc3OjXLly7Nu3j127dhEUFMSGDRswm81UrVqVRYsW\nAZCamkpiYiKJiYm0b9+e1q1bU716dcqWLcuKFSsAWLBgAREREWzbto0+ffowc+ZM4/0XN6Wnpxs/\n//WJthaLhRkzZlChQgUALl++jIeHB46OjlSpUoWtW7eybNkyfvrpJyZPnpwTu0bkvuiaguR6AQEB\nzJo1i4oVK+LgcOP3nNTUVCIiIjh9+jRw44A/ffp0zpw5g8lkon///rz44ots27YNs9lMbGwsAQEB\n+Pv7G0+sPXbsGF5eXpw6dQqr1UpUVBTHjh27bQ0vvvgiK1euBCA6OppWrVpx4cIFhgwZwsGDB+nc\nuTODBw/mt99+y5mdInKfNFKQXK9+/fqMGTOGwYMHG9MKFChAcHAwQ4YMwWKxULhwYWbMmEG+fPl4\n5plnCAgIwNnZmerVq/Pnn3/i7e1N586d6dChA3nz5qVo0aK0bdsWJycn1q5dS7NmzShdujTVqlW7\nbQ1vvfUWEyZM4JVXXsFsNvP222/j4+ND//79GTNmDAsWLMDe3j7TRWuRR5EecyEiIgadPhIREYNC\nQUREDAoFERExKBRERMSgUBAREYNCQUREDAoFERExKBRERMTw/3zrHQZk3pVyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113465f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HjuZr6dNPP001001Rvnx5Jk6cyFtvvcXUqVPvue6HncVIy1xQMqV9+/axe/duOnToAMDM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b51278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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169YBsHPnToKDg837ihQpQlxcHMePHwdg69atBAUF3bY/HVMQEcnCGjRowPr1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\nhEVCQQghhEVCQQghhEVCQQghhOUHDJIWLBAhJYAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ce0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hw5gxYwbHjh2jX79+7N+/n4iIiMyuTUREsliGvry2d+9elixZwtSpU2ndujU9\ne/akVatWmV2biMgDx1LZF0uyI2Nt3X0zuZrbZWikYLfbcTgcrFu3jho1apCYmEhiYmJm1yYiIlks\nQ6HQvHlzqlevzqOPPkqZMmVo2bIl4eHhmV2biIhksQwdPurcuTMdO3bEZrMBMHfuXPz8/DK1MBER\nyXoZGimcOXOGV155hQYNGhATE0Pv3r05ffp0ZtcmIiJZLEOhMHToULp06YKnpycFChTg+eef19VH\nIiIPoAyFwuXLl6levToAFouFNm3aEBcXl6mFiYhI1stQKHh4eHDu3DksFgtw4wtsbm5umVqYiIhk\nvQydaB44cCDdunXj1KlTNGvWjKtXrzJ58uTMrk1ERLJYhkLhqaeeYvHixZw4cQK73W7+CLSIiDxY\nMnT46JdffuHLL7+kWLFijB8/ntDQUFavXp3ZtYmISBbLUCiMGjWKUqVKsXr1ajw8PFi6dCkff/xx\nZtcmIiJZLEOh4HA4qFSpEj/88AMNGjSgSJEi2O32zK5NRESyWIZCIVeuXHz22Wf8/PPP1K5dm9mz\nZ+Pl5ZXZtYmISBbLUChMnDiRhIQEPvjgA3x8fIiJieHdd9/N7NpERCSLZejqo7x581KvXj1KlCjB\n8uXLcTgcWK0Pxo+29ajngvvVDG2GbCEKGNw059RrfSXQ2SX8ZbuAod+GOLuMv8Q4esXZJfxlu4Eh\n7z7i7DIyJNndjwPOLiKLZGjP/tZbb7F69Wr27NnDlClT8Pb2ZsCAAZldm4iIZLEMhcLp06fp1asX\nq1evpnXr1rz++utcvXo1s2sTEZEsluEf2YmNjWXdunXUqlWLCxcukJSUlNm1iYhIFsvQwekuXbrQ\npk0b6tSpQ0hICA0bNqRXr16ZXZuIiGSxDIVCWFgYYWFh5uOVK1eSmpqaaUWJiIhzZCgUVq9ezbRp\n00hISMAwDBwOB4mJiWzdujWz6xMRkSyUoVCYMGECo0aNYtasWbz66qv89NNPXL58ObNrExGRLJah\nE8158uTh2WefpUyZMly/fp2ePXsSFRWV2bWJiEgWy/CP7Pz2228EBQWxbds2UlJSuH79embXJiIi\nWSxDodC7d2/ef/99ateuzZYtW6hWrRr16tXL7NpERCSLZeicQqVKlahUqRIAS5Ys4erVq/j4+GRq\nYSIikvXuGgodOnQwf5f5z3zxxRf3vSAREXGeu4ZCz549uXr1KmlpaeTLlw8AwzC4dOkS+fPnz5IC\nRUQk69xdU1GCAAAgAElEQVT1nIK3tzcjRozAy8vLPIS0efNmxo4dS548ebKqRhERySJ3DYVx48bx\n7rvvUqNGDXNanz59GDNmDJGRkZlenIiIZK27Hj66du0alStXvm16aGgoEydOzLSiREQeVJaAPFjs\njoy1tWX9EZm7jhTS0tJwOG4v3uFw6N5HIiIPoLuGQsWKFZk6dept06dPn07p0qUzrSgREXGOux4+\n6tu3L127dmX58uU89dRTGIbBgQMH8PPzY8aMGVlVo4iIZJG7hoK3tzdz585l69atHDx4EKvVSvv2\n7XnmmWeyqj4REclC9/xGs8VioUqVKlSpUiUr6hERESfK0L2PRETk4aBQEBERk0JBRCQHczgcDB06\nlPDwcDp06MDJkyf/tN2QIUMy9P0yhYKISA62du1aUlJSWLhwIf369fvTu00sWLCAI0eOZKg/hYKI\nSA62c+dOQkNDAShbtiz79u1LN3/Xrl3s2bOH8PDwDPWnUBARycHi4uLw9vY2H9tsNtLS0gCIiYlh\n2rRpDB06NMP9ZehHdkREJHvy9vYmPj7efOxwOHBxubFr//7777l8+TJdu3blwoULJCUlERgYSMuW\nLe/Yn0JBRCQHK1++PBs2bKBJkyZERUUREhJizuvYsSMdO3YEYOnSpfz66693DQRQKIiI5Gj169dn\n06ZNtG3bFsMwGDNmDMuXLychISHD5xFupVAQEcnBrFYrI0eOTDctKCjotnb3GiGY/d2XqkRE5IGg\nUBAREZNCQURETAoFERExKRRERMSkUBAREZNCQURETAoFERExKRRERMSkUBAREZNCQURETAoFEREx\nKRRERMSkUBAREZNunS0ikpUCnwBrQsbaOjwh/t7N7ieNFERExKRQEBERk0JBRERMCgURETEpFERE\nxKRQEBERk0JBRERMCgURETEpFERExKRQEBERk0JBRERMCgURETEpFERExKRQEBERk0JBRERMCgUR\nETHpR3ac6Lvz1xl8IIZkh8FTedyZWbYIeVxt6drsvZZEr73nuJbqwGaB3gcPUvYP/bTeFk0RDxc+\neLowAAeuJ/Pqnt+JT3NgwcLoJx+h4SPeWbRW2ct3288z6ItDJKc5eLpYHj5542nyeLqmazN1xW98\nuOokFouFoEKe9BgVC0Ds9RRem7GXqN+u4eVuo1O9ovR8PgCA5dvO0+n9KPwL5DL72Ti2Krk9H75/\nUt8diOXtlSdITjN4qrAnn4QHk8fjz7fDsr2X6DT/CFfGVAEgNiGV1xYfZ8/v8Xi5WelUsSA9Qouk\nW+azn8+xbN8lvu1SKtPXRTRScJoLyWm8svt3vqr4GAfqPkGAlxuDDsaka5OQ5qDxllO8+UQ+dtQK\n5O2QAgwePDhdmwlHL/JTbPpfcerxy1k6+/uys1YQM8sVpt2O06Q5jExfp+zmwtVk/vXBHhYPrMCh\nGbUJKOTJgNmH0rXZeewK7y77lU3jq7F3ak2eKOLFjBkzAOj7yQG8PFzYP7UWWyZU5/udMazYfh6A\nzQdj6dc8kN2Ta5j/PYyBcCEulS4Lj7Lo5ZIcHFCBwHweDPzuxJ+2PXohkf7Lf8Nh/O+92Peb3/B2\nt7Gvf3k2v1GGVYcus+LA/4dyQirdFx+j17JfMR6+t6/TZEkoJCcnU6dOnT+d9/PPP9OnT5/7/pwT\nJ05k6dKl973f++U/F+J5xjcXwd7uALz6eF7mnb6Kccu7/z8X4gj0cqVJwdwAhBXyJjIy0py/4WI8\nqy/E0fXxvOn6thsGl1McAFxPc+BhtWT26mRLa3ZfoGKwL8FFboySujcuxrz/nkm3jSs84cuRD2vj\n4+VKUoqd3y8l4evrC8DO41fpUPtRbDYLbq5WmjxTkCWbzgKw5dBlNuy9xDN9fqTGgM1s3Hcp61cw\nG1hz+DLPFPUm+P9HTK9WLcy8XRfSbWOAhBQ7HecdZmLTgHTTd52O46UKBbBZLbi5WHmuZF6W7LkI\nwFdRFymc243xYemXkcylkYKTRCemUjTX/w5jPObhyrU0B9fTHOa0I3EpFHJ34d9Rv1P5v7/ScMsp\n7HY7AL8npdJ37znmlH8U2x/6nvJUYcYdu0ixNUdouPkk054ujMtDGAzRF5N4LL+H+fix/B5cS0jj\nemJaunauLlaWbT1H0c5r2bj/EmFhYQBUCvFlzoYzpKY5iEtMY+nms5y9nAxAvtxuvNakGDsmhTKm\nYwlajt3B6YuJWbdy2cTpK8kU9XU3Hz/m4861JDvXk+3p2r26+Bj/frYQTxfxSje9kn9uvtx5gVS7\ng7hkO0v3XuLstZQby1QtzNCG/uRy0W4qK2XaeDc+Pp4333yTa9eu4e/vD8Dhw4cZNWoUAL6+vowZ\nMybdMl9++SVr1qwhMTGRvHnzMnXqVAYOHEhYWBi1atXi+PHjjBs3jmnTpjFs2DBOnjyJw+Ggd+/e\nVK5cmdWrVzNjxgz8/PxITU0lMDAws1bvH3PcYTxss/xv551qGKyKiWNt1WJUzuvJt2ev8+9evWjx\nbAFe3HGGd0sXorBH+uPjSXYHL+48zadli/B8odxsjU2g+bZonsmbK10IPQwcdzhkZvuTgGz+bCGa\nP1uImatP0rNnT5pOK8u7/3qSN2cdoHzvHymc1516ZQuw5dCNQxtLBj1jLlv9ST+qlsjLf6Iu0rle\n0cxZmWzqTkclb30fz9h0FherhX9VLsSJ2KR07SY2DeCtb3+jwntRFMrtRr0QXzafuJaZJcs9ZFoo\nLFiwgJCQEPr06cOePXv4+eefGTJkCGPGjOGJJ55g0aJFfPLJJ1StWhUAh8PBlStX+Pzzz7FarXTp\n0oW9e/fywgsvMH/+fGrVqsXixYtp3bo1ixYtIm/evIwZM4bLly/z0ksvsWzZMiIjI1m6dCm+vr50\n7do1Q3Ue7PVeZm2CuzJWruTQ2rVEDb3x/GfPniXPxvYcHfml2Sbt22/xX7gQ90lziQL8AXu9eixs\n1Z+jOwbS45I7XErj0iUDu93gjCWYVuGtuLKzP49N/5oowAPw79qVhXXbUK9ePWesqtM4QlZy6NRa\ndhUZDvz/Ns7zE4cDR5ltoqOjuXTpEmXL3jh9X7ajnbMzqvCD92ySXJJoPyAXr/n4APD555+T+4lL\n/Dd3VxYtWkTnzp2x/P/O74p7b87kb8CuIk2ydiVvKnLvJpnBEX/jfby75i3v4zztOdJgitlm+qcd\nSUoyKPlRDKmpqSSmQcmPYpj85AXOlR1M+yrpt3Een0vsrtnPXP7UteVcO7OO3TXfz9qVe0hlWiic\nOHGCmjVrAlCmTBlcXFw4fvw4I0aMACA1NZXHH3/cbG+1WnF1daVv3754enpy7tw50tLSqFy5MqNG\njSI2NpZNmzbRt29fRo8ezc6dO/nll18ASEtL48KFC/j4+JA3743j6+XKlctQnSUn98X96sX7uOYZ\nUyQ5jalbj+PVP5xgb3e+OnCeFj4Wyo7saLYplJTGlF+PYe/7AhV8c7HxUjwWi4U2S8bTsXI+s92I\nQxYupVj4wDjKlfmj6XkxhoTeranq58nx+BRO7/uNFnmvEbT5iyxfT+srzhutPfZ4MlP3bCX3jjcJ\nLuLNwNkHaVkxD+V/H262iT90iR4Td7N7cg3y53Hji/WnCQoKok7C+wz64hDXElKZ+upTnL+czKrF\nm5j3VnnKX5lI+wVrqZ3nZ1pVLczu41c5vHcbS1/1JP/v25yyrsbRK0553kcdKUzdtRvvxd0ILpCL\nRd+doEVxD8r9t7fZZm8XP/PvE7FJPD3hJAe7PcLuAgVYHNGca0l2prQM4vz1FFbN/4W5LxVPt/ye\nQ+fJc+liumlZLdndjwPPDnXa82elTAuFoKAgoqKiqFevHgcOHCAtLY2AgADGjRtHkSJF2LlzJxcu\nXDDbHzp0iLVr17Jo0SISExNp2bIlhmFgsVho2rQpo0aNolq1ari6uhIYGEihQoV49dVXSUpKYsaM\nGeTPn59r164RGxuLn58fe/fupVChQpm1ev/YI+4ufFKuCOE7TpPiMAj0cuPzco+y40oi3aJ+Z2et\nIAp5uLCkUlF6/HKWBLuBu9XChHen4vHtnUc3vq42FlcqSt9950iyG7haLcwoU5ggL7csXLvs4RFf\ndz7rVYYXIneSkmYQVMiT2X3KsuPoFf499Rd2T65BaKl8DHrhCWoP2oKLzUIRP3cmTvwEmMnA1k/Q\ncdJunurxXwzDYFi7ECoG3zgJveztirzx8T6GzzuCi83CgrfKkz/PQ7iNc7vxadtg2sw+SIrdIDCf\nB7NfDGFH9HW6fnWMXf3u/uFsQJ3H6DjvCE9P2IVhwNCG/lT0z51F1cufsRh/vEzgPklOTqZ///7E\nxMQQGBjIjh07ePfddxk3bhxpaWlYLBZGjx5NTEwMCxYsYMyYMXTr1o2UlBsnmdzc3GjdujVNmzbl\n4sWL1KpVi2+++YagoCBSUlIYPHgwv//+O3Fxcbz44ou0adOGH374gcmTJ+Pj44OLiwtNmjShZcuW\nd6xv3759Thsp/F1RQ79IN5rI7pw5Uvi7dhUZnm40kRM4a6TwT+yu+b5TP/3/FTdHCqVLl8bd3f3e\nC/xZH/+/zynltQJ3a8K9FwCSHZ7sj3/+Hz3vX5VpIwV3d3cmT5582/Q5c+akexwQEEDlypUB+OKL\nPz+8YbfbqVChAkFBQcCNwBg/fvxt7WrVqkWtWrX+YeUiIg+vbH+t15o1a3jllVd44403nF2KiMgD\nL9t/BbNBgwY0aNDA2WWIiDwUsv1IQUREsk62HymIiDxILHmDsbimZqxtqivEZ3JBf6CRgoiImDRS\nEBHJwRwOB8OHD+fw4cO4ubkxatQoihUrZs5fsWIFs2fPxmazERISwvDhw7Fa7zwe0EhBRCQHW7t2\nLSkpKSxcuJB+/fqlu5NyUlIS77//Pl988QULFiwgLi6ODRs23LU/hYKISA62c+dOQkNDAShbtiz7\n9u0z57m5ubFgwQJy5bpxa/O0tLR7fglOoSAikoPFxcXh7f2/X1a02Wykpd24PbzVaiV//vzAjS8O\nJyQkUK1atbv2p3MKIiI5mLe3N/Hx/7tEyeFw4OLiku7xhAkT+O2335gyZYp5Z9870UhBRCQHK1++\nPBs3bgQgKiqKkJCQdPOHDh1KcnIy06dPNw8j3Y1GCiIiOVj9+vXZtGkTbdu2xTAMxowZw/Lly0lI\nSKB06dIsXryYZ555hpdffhmAjh07Ur9+/Tv2p1AQEcnBrFYrI0eOTDft5s1D4cbPEvyl/u5LVSIi\n8kBQKIiIiEmhICIiJoWCiIiYFAoiImJSKIiIiEmhICIiJoWCiIiYFAoiImJSKIiIiEmhICIiJoWC\niIiYFAoiImJSKIiIiEmhICIiJoWCiIiYFAoiImJSKIiIiEmhICIiJoWCiIiYXJxdgIjIQ8W3OLhn\nsG0ycDozi7mdRgoiImJSKIiIiEmhICIiJoWCiIiYFAoiImJSKIiIiEmhICIiJoWCiIiYFAoiImJS\nKIiIiEmhICIiJoWCiIiYFAoiImJSKIiIiEmhICIiJoWCiIiYFAoiImJSKIiIiEmhICIiJoWCiIiY\nFAoiImJSKIiIiEmhICIiJoWCiIiYFAoiImJSKIiIiEmhICKSgzkcDoYOHUp4eDgdOnTg5MmT6eav\nX7+eVq1aER4ezldffXXP/hQKIiI52Nq1a0lJSWHhwoX069ePyMhIc15qaipjx47ls88+Y86cOSxc\nuJCLFy/etT+XzC44uzIMA4CU3HmdXMlfl+yT39klZJjV5uvsEv6W5BxWt+GeMz/fJbv7ObuEDElx\n9wH+t9/4J1JT72/bnTt3EhoaCkDZsmXZt2+fOe/48eP4+/vj43Oj/goVKrB9+3YaN258x/4e2lBI\n/f+tffxfQ5xcyV93sNd7zi7hgbe/YG9nl/DXFHR2AX/PgWeHOruEvyQ1NRUPD4+/tazNZsNms3H4\nsP1vLXcncXFxeHt7p2uflpaGi4sLcXFx5M6d25zn5eVFXFzcXZ/voQ0FLy8vQkJCcHV1xWKxOLsc\nEcnGDMMgNTUVLy+vv92Hi4sLpUuXxm7/66Hg4nLnXbW3tzfx8fHmY4fDYbb/47z4+Ph0IfGndf6l\n6h4gVqv1nhtHROSmvztCuJWLi8tdd/B/R/ny5dmwYQNNmjQhKiqKkJAQc15QUBAnT57kypUreHp6\nsmPHDrp06XLX/izG/ThIJiIiTuFwOBg+fDhHjhzBMAzGjBnDgQMHSEhIIDw8nPXr1zNt2jQMw6BV\nq1a0b9/+rv0pFERExJQzL1kQEZFMoVAQERGTQkFEREwKBZF7OHbsGGlpac4uQyRLKBQeQH/1Omi5\ns1WrVjFjxgz279+vYMgif7z2xeFwOKmSh5NC4QHjcDiw2WwYhsGePXs4d+6cs0vKkQzDYNKkSdSt\nW5enn36ab775RsGQBRwOBxaLhevXr3P27Fni4uKwWrWbykra2g8Qu92O1WrFMAzeeOMNIiMjmTlz\nJmvXrnV2aTmOxWLh8OHD9OvXj3bt2uHv78+yZcsUDJnMarVy/vx5unTpwvz582ndujXHjh0D7s99\nh+TeFAoPkJsjhE8//ZQKFSrw+eefU7x4caKioli9erWzy8sxbt4X68MPPyR37ty88cYbvPjiixQr\nVozly5cTFRWlQ3T32c1DRCkpKYwdO5bXXnuNV155BbvdzoIFC4iPj9ftaLKIQuEBcOsx1x07drBw\n4ULc3d1xd3enbt26FC5cmJ07d3Lp0iUnVpkzOBwOXF1diY2N5cyZM4wZM4ZixYrRs2dPXnzxRQoU\nKMC6des0WriPHA4HVquVy5cvY7VaKVmyJEePHuWNN95g1qxZBAQEsGPHDmeX+dB4aO999KCw2+3m\nCOHw4cOEhIQQERHBvHnzCA4O5plnnqFx48YkJyeTL18+Z5ebrRmGgdVqJSYmhldffZUnnniCtLQ0\n3nvvPSIjI+nUqROff/45CQkJuLu7O7vcB8bNQIiIiKBly5Z4eHiwfPlyGjduTExMDAsXLmT69OnO\nLvOhYRs+fPhwZxchf5/VasXhcNCtWzcOHz7MlClTqFmzJv7+/nz00Uc89thjhISE6OZ/GXDzBGdE\nRARdunShXr16fPPNN+zdu5e3336b6OhogoKCKFCggLNLfaCkpKQwYMAAvL29ee211yhWrBiJiYmk\npKTw/fffM3LkSB5//HFnl/nQ0L2Pcqjo6GgKFy6Mi4sLY8aMwWazERERwfbt2xk7diwTJkzg4MGD\nFC1alDJlyji73Gzt5mgLbtybfuXKlQQEBLB06VJCQ0P5/PPP8ff3Z+LEiU6u9MFxc5unpqbi6urK\ntm3bmDZtGi+99BL169c3p1+/fl0faLKYDh/lQFu2bCEuLo6iRYsCUKRIEVxdXQGoWLEiderUYe/e\nvTRv3tyZZeYINy/hPX/+PBs2bKBYsWKEhITw008/0apVKxwOByEhIXTt2tXZpT4wbm7zc+fOMWHC\nBBISEmjevDnNmjVj3rx5pKam0rBhQ4B0Px4jWUMnmnOgKlWqUL9+fWbPns2mTZvw9PQkJiaGtWvX\nsm3bNtasWYO/v7+zy8wRrFYrFy5coH///pw8eRIvLy/Kli1LfHw8y5cvZ+jQoXTu3Fnb8z66eQ7h\n7bffplatWnTv3p3ly5fj4eFBly5d+Pbbb0lKSgLQFUdOoJFCDnLrYQ6AhIQEfvrpJ6pVq8bly5fZ\nu3cvBw8eJCIigvLlyzux0pzh5lUvS5cu5cknnyQiIgKAbdu24e3tTWhoKF27duXRRx91cqUPhpvb\nG+DMmTPkypWLsLAwALp3705kZCRz5syhfPnyeHp6OrPUh5pCIYe4eYz15g9qPPnkk3Tv3p3PP/+c\n7du3U7lyZapWrcq1a9fIkyePs8vN1m7unG6eTvP398dqtZKQkICnpye7du2iSJEiVK1a1cmVPjhu\nbvPY2FguX76M3W6naNGibNmyhSpVqhAbG4uXlxeJiYkKBCfTieYc5vXXX6dixYoEBgaSlJREQEAA\n3333HTExMQwYMIDcuXNryH0XN3dOMTExzJ07l+LFi5OYmMiqVauoXr06CQkJbN26leHDhxMYGOjs\nch8oNy/1rV69Ok8//TSnTp3iypUrREdHc/nyZQYNGpTupyTFORQK2dyyZctITEykXbt2nDt3jgED\nBjB8+HAiIyMpWrQoMTExjB8/nkuXLlGkSBFnl5ut3fpptXPnznTu3Jn169dTrFgxgoODcTgcnD59\nmueee46AgABnl/vAuPmj94MGDaJMmTJ06NABgJkzZxIcHIyvry8FCxakcOHCTq5UQIePsr2bVxC9\n99579O3bl6effpotW7bQrVs3goOD6d69O7GxsQqEe7gZCFeuXOHw4cO0bt2a5s2bs3DhQsqUKUOB\nAgWoUqWKs8t8oNw8B2axWHBzc+Pxxx8nb9685msRFxeHv7+/RmTZjK4+yqb+eG+do0eP8tprr5nB\nsGHDBjp27Mgrr7yiT1gZcPOKl65du7J//34+/vhjwsLCmD59OoGBgcydO5f4+HjddO0+uRkI58+f\nZ+bMmcTGxuLn58f+/fv5z3/+wzfffMOPP/6o8wfZkEIhG7p5HbfD4WDkyJFMnTqVyZMn4+npSdeu\nXSlVqhSNGjVi9OjR1KxZ09nl5ggOh4Pvv/+etLQ06tWrxxtvvEF8fDxr165l8uTJ9O7dGy8vL52P\nuU9sNpt5nsswDI4cOUKDBg3w8vIiOjqadevW8e6771KoUCFnlyp/oHMK2Vj37t0JDg4mNDSUihUr\nkpSUREREBLGxscyZM8fZ5eU4sbGxfPXVV1y5coVmzZoBcOLECUqVKqXvIWSCjz/+mJiYGDp27MiQ\nIUOoWLEiBQsW5IUXXiAxMZFcuXI5u0T5ExopZCO35vPZs2exWCz07duXihUrcvToUYYOHcrkyZMZ\nPHiwE6vMufz8/GjTpg2FChVi/vz5eHh40LhxYwVCJvHz8yN37txMnTqVt99+GxcXF37//XcAPDw8\nnFyd3IlCIZuw2+3pDl088sgjeHt789577wE3/hFdu3aNK1euULx4cWeVmeP5+fkRFhZGcHCwvs9x\nn928hfvN/z/77LN07tyZsLAwjh49ys8//0zTpk0BfVM5O9Pho2zAMAwsFgsOh4M33niDwMBAjh07\nRteuXVmwYAHXrl3jwoULdO/enTp16ji73AfCH78dLn/frFmzaNmyJT4+PuZ23bFjB4sXL+bVV1/l\nwIEDHDt2jMaNGxMcHOzscuUedEmqk90MBID+/ftTpUoVGjduzAsvvMC6desYMWIER48exdvbW7cP\nvo8UCPdHXFwca9asISYmhm7duuHr68uFCxeYMmUKnTp14vHHH+fxxx9P9z6X7E0jBSe69V4wAFOn\nTuWZZ57hyy+/pGXLlsTGxlK6dGlKlCjhxCpFbudwOJg1axaBgYFMmTKFatWqceXKFfr164evry+/\n//47RYoUURjkQDqn4CQ3f+XLMAwGDRrEsmXLiI2NZdy4cVSpUoVSpUrxxRdf6Lp5yZYmT57M7t27\nKVeuHG3btuWll17Cz8+P9957jytXrlCkSBEcDocCIQdSKDjBrSeV33zzTaxWK82bN6d9+/YULVqU\nuLg4+vXrR79+/ShZsqSTqxW5XbNmzTh16hQDBgygVKlSFCxYkBYtWlCgQAFGjRrF1atX042CJefQ\nOYUsdusX07Zt28aZM2dIS0vj5MmTBAUFMXDgQDw8PKhXrx5BQUHOLlfkTxUrVgwXFxeOHTtGbGys\nOa1Jkyb85z//ISUlxckVyt+lcwpZ6ObxVcMw6N69O4888ggxMTH88MMPlC5dmkmTJpm/piaS3cXG\nxhIdHc3YsWPp3Lmz+WtpN2/zLjmTQsEJpk6dypkzZxg7diyGYdC7d282btzIo48+yuLFi/XFHslR\nNm7cSGRkJP369aNu3brOLkf+IR0+ymLXr18nOTmZS5cucejQIUqUKEFYWBiNGjWidOnSCgTJcWrU\nqIGrq6tGuQ8IjRSc4OrVqyxdupTo6GiCgoL45ptv6NWrF9WqVXN2aSLykNPlAU7g4+NDs2bN8PPz\nY+XKlbzwwgtUq1ZNl5+KiNPp8JGT+Pn58dJLL+Hp6cnhw4fNQ0kiIs6kkYIT+fr60qxZMx5//HHy\n58/v7HJERHROITvQzdlEJLtQKIiIiEmHj0RExKRQEBERk0JBcqTTp09TvHhxhg4dmm76wYMHKV68\nOEuXLnVSZSI5m0JBcixfX19+/PFH7Ha7OW3lypX4+fk5sSqRnE2hIDmWl5cXJUuWZPv27ea0TZs2\nUbVqVeDGPXlat25N8+bN6dGjB5cvXwZg1apVtGnThqZNm9KwYUNz+VmzZtG0aVOaN29ujkCWLl3K\ngAEDzP47dOjAzz//zM8//0zr1q1p2bIlERERxMfHExERQcuWLWnWrBkrVqwA4NChQ7Rp04aWLVvS\nrl07Tpw4kRWbRuRv05fXJEdr3Lgxq1ev5tlnn+WXX36hePHiGIZBbGwss2fP5osvvsDHx4cFCxYw\nceJE3nnnHRYsWMCHH36In58fixcv5tNPP6VcuXJ89NFH/Pjjj9hsNkaMGMH58+fv+twnTpxgw4YN\n5M6dm4kTJ1KqVCnGjRtHXFwcbdu2pUyZMsyePZvOnTvTuHFjVq5cSVRUlH5WVbI1hYLkaLVr1+b9\n99/H4XCwatUqc+fr4eHB2bNn6dixI3Djdyx8fHywWq1MmzaN9evX89tvv7Ft2zasVisuLi6UK1eO\n1q1bU7duXdq3b0/BggXv+twBAQHkzp0bgM2bN5OUlMSSJUsASEhI4OjRo9SsWZORI0fy448/Urt2\nbfP20iLZlUJBcjRvb29KlCjBzp072bp1K/369WPlypXY7XbKly/Phx9+CEBycjLx8fHEx8fTqlUr\nmjVrRsWKFSlevDhz584FYPr06URFRbFx40ZeeeUVJk6caP7+xf+1c/+u5sVxHMefcYmFSMmiLEos\nkhe3lRYAAAGESURBVDqrzSkpZTh/whkUy53OYpDByWo3mfwN4i+QQRlEKUqyS7jD7Xv6futOd7h9\nb70e06fP9O4zfF6fH/X+436/e+O/O9o+n09c1yWfzwNwuVyIRqMEAgGKxSKz2YzxeMx8PqfX6/3E\n0oh8i/4U5NczTZPhcEihUODt7fOcc7vdWC6X7HY74HPDHwwG7Pd7fD4ftm1jGAaLxYLH48H1esU0\nTbLZrNexdrPZEIvF2G63vF4vDocDm83myxoMw2AymQBwPp+p1+ucTic6nQ6r1QrLsmi326zX659Z\nFJFv0k1Bfr1KpYLjOLTbbW8ukUjQ7/fpdDo8n0+SySSu6xKJRMjlcpimSSgUolwuczweicfjWJZF\ns9kkHA6TSqVoNBoEg0Gm0ynVapVMJkOpVPqyhlarRbfbpVar8Xg8eH9/J51OY9s2juMwGo3w+/3/\nfFqL/I/U5kJERDx6PhIREY9CQUREPAoFERHxKBRERMSjUBAREY9CQUREPAoFERHxKBRERMTzAWm8\nh3p1lRknAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a865f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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68+qlbUYeWscAf3w/3NwvL23LwD051JwcfSXHltvf35/IyEji4uLw9PRk586dd30mygMb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\nIYQQKgkFIYQQql/QWduCoflPGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14f01748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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E8liODh/17duXPn36YLPZAJgzZw6+vr65WpiIiOS9HM0UTp8+Tb9+/WjZsiUx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16404f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsWMEBwfz+++/ExISktW1iYhINsvUh9f279/PN998w9SpU2nfvj39+/enXbt2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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x165106a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K1K/jFBGR9Ao85Y7rlYQMzZvo4c7FTK7nnzRSEBERk0JBRERMCgURETEpFERE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\nANU8QwHyfo0VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14ef37b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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s2DFatWrFpUuXmDlzZmbXJiIiWSxDofDwww+zfPlyjhw5gt1uN28CLSIi95YM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfb69128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ExKRQEBERk0JBRERMCgURETEpFERExJThUIiOjgZufvhhwYIFJCYmZlpRIiJi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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16421668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IFGBr164lLS2NRYsWMWTIEMLCwsx5hmEwcuRIxo8fz4IFCwgMDCQqKirH9jRS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\n3sr2VZJiHwqFO9yyZctITk6mS5cunD9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14f0ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the classification reports\n",
    "for clf in clfs:\n",
    "    plt.subplots(figsize=(6,5))\n",
    "    i=clfs.index(clf)\n",
    "    mhf.visual_model_selection(X_tr_clf_sel, X_ts_clf_sel, y_tr_clf_sel, y_ts_clf_sel, clfs[i]())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification Candidate Models:\n",
    "1. ExtraTreesClassifier\n",
    "2. MLPClassifer\n",
    "3. BaggingClassifer\n",
    "4. RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of true positive df:  (171, 2) Number of 1's in true positive df: delayed    171\n",
      "pred       171\n",
      "dtype: int64\n",
      "Now getting train test splits for regression...\n",
      "Shapes:\n",
      " (1015, 725)\n",
      ",(171, 725)\n",
      ",(1015,)\n",
      ",(171,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1662b3c8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nd3d3OnXqRLVq1Qq0cBEREWdlUzh7eXnx4Ycf3vP8K6+8Yvm5f//+9O/f3/bKREREiihN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\nEANDxZkQQggxMFScCSGEEAPzP2PR+F32wBLsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x54089e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First generate true positives to predict on \n",
    "# Use favorite classifier selected thus far\n",
    "estimator = ExtraTreesClassifier(random_state=121)\n",
    "df_true_pos_sel = mhf.fit_and_generate_true_positives(estimator, X_tr_clf_sel, X_ts_clf_sel, y_tr_clf_sel, y_ts_clf_sel)[0]\n",
    "# Get training and test sets...\n",
    "X_tr_reg_sel, X_ts_reg_sel, y_tr_reg_sel, y_ts_reg_sel = mhf.train_test_conditional(X_clf,\n",
    "                                                                                    y_clf, df_true_pos_sel, ddate, delayed)\n",
    "\n",
    "# List of regressors\n",
    "regs = [LinearSVR, SVR, KNeighborsRegressor, LinearRegression , SGDRegressor,\n",
    "        BaggingRegressor, ExtraTreesRegressor, RandomForestRegressor, MLPRegressor]\n",
    "# List of regressor names\n",
    "rn = ['LinearSVR', 'SVR', 'KNeighborsRegressor', 'LinearRegression', 'SGDRegressor',\n",
    "    'BaggingRegressor', 'ExtraTreesRegressor', 'RandomForestRegressor', 'MLPRegressor']   \n",
    "# Plot the f1 scores\n",
    "rsq = []\n",
    "for reg in regs:\n",
    "    try:\n",
    "        rsq.append(mhf.model_selection(X_tr_reg_sel, X_ts_reg_sel, y_tr_reg_sel, y_ts_reg_sel, reg(), 'reg'))\n",
    "    except:\n",
    "        rsq.append((0,0))\n",
    "d_rsq = pd.DataFrame(rsq, rn, columns=['r2', 'rmse'])\n",
    "f , (ax0,ax1) = plt.subplots(2,1, figsize=(8,6), sharex=True,)\n",
    "d_rsq['r2'].sort_values().plot(kind=\"bar\", ax=ax0, ylim=(-1,1), title=\"R-Squared\")\n",
    "d_rsq['rmse'].plot(kind=\"bar\", ax=ax1, title=\"Root Mean Sqaured Error(RMSE)\", ylim=(0,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BaggingRegressor</th>\n",
       "      <td>0.905797</td>\n",
       "      <td>9.601237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreesRegressor</th>\n",
       "      <td>0.895309</td>\n",
       "      <td>10.121583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestRegressor</th>\n",
       "      <td>0.882198</td>\n",
       "      <td>10.736692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPRegressor</th>\n",
       "      <td>0.837071</td>\n",
       "      <td>12.626789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsRegressor</th>\n",
       "      <td>0.791609</td>\n",
       "      <td>14.280179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDRegressor</th>\n",
       "      <td>0.730774</td>\n",
       "      <td>16.231279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearRegression</th>\n",
       "      <td>0.671484</td>\n",
       "      <td>17.929674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVR</th>\n",
       "      <td>0.620708</td>\n",
       "      <td>19.265538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR</th>\n",
       "      <td>-0.041528</td>\n",
       "      <td>31.924910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             r2       rmse\n",
       "BaggingRegressor       0.905797   9.601237\n",
       "ExtraTreesRegressor    0.895309  10.121583\n",
       "RandomForestRegressor  0.882198  10.736692\n",
       "MLPRegressor           0.837071  12.626789\n",
       "KNeighborsRegressor    0.791609  14.280179\n",
       "SGDRegressor           0.730774  16.231279\n",
       "LinearRegression       0.671484  17.929674\n",
       "LinearSVR              0.620708  19.265538\n",
       "SVR                   -0.041528  31.924910"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_rsq.sort_values('r2', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression Candidate Models\n",
    "1. ExtraTreesRegressor\n",
    "2. BaggingRegressor \n",
    "3. MLPRegressor\n",
    "3. RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 4. Model Refinement\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Final Model and Evaluation Metrics\n",
    "(i) Classification: ***ExtraTreesClassifier***\n",
    "(ii) Regression: ***ExtraTreesRegressor*** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XTrain, yTrain shapes BEFORE Oversampling: (6710, 725) and (6710, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:75: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XTrain, yTrain shapes AFTER Oversampling: (11858, 725) and (11858,)\n",
      "Shape of XTrain: (11858, 725) yTrain: (3614, 725) XTest: (11858, 1) yTest: (3614, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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5Y38KBRGRLMxqtTJq1Kg0bYGBgebP1apVY9GiRenv775VJiIiWZ5CQURETAoF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11379c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of true positive df:  (221, 2) Number of 1's in true positive df: delayed    221\n",
      "pred       221\n",
      "dtype: int64\n",
      "Now getting train test splits for regression...\n",
      "Shapes:\n",
      " (965, 725)\n",
      ",(221, 725)\n",
      ",(965,)\n",
      ",(221,)\n",
      "---\n",
      "R-Squared:  0.858493976467  ---\n",
      "RMSE: 11.8156414684\n"
     ]
    }
   ],
   "source": [
    "clf_final = ExtraTreesClassifier(n_estimators=900,max_features= 50\n",
    "                                  ,criterion= 'entropy',max_depth= 50, random_state=121)\n",
    "reg_final = ExtraTreesRegressor(n_estimators=900 ,max_features= 50,max_depth= 50, random_state=121)\n",
    "X_tr_clf_fin, X_ts_clf_fin, y_tr_clf_fin, y_ts_clf_fin = mhf.train_test_oversample(data\n",
    "                                                        , delayed, test_size=0.35, use_smote=True)\n",
    "plt.subplots(figsize=(6,5))\n",
    "score_fin=mhf.visual_model_selection(X_tr_clf_fin, X_ts_clf_fin, y_tr_clf_fin, y_ts_clf_fin, clf_final)\n",
    "\n",
    "df_true_pos_fin = mhf.fit_and_generate_true_positives(clf_final, X_tr_clf_fin, X_ts_clf_fin,\n",
    "                                                      y_tr_clf_fin, y_ts_clf_fin)[0]\n",
    "X_tr_reg_fin, X_ts_reg_fin, y_tr_reg_fin, y_ts_reg_fin = mhf.train_test_conditional(data,\n",
    "                                                                    delayed, df_true_pos_fin, ddate, delayed)\n",
    "rsq = []\n",
    "rsq.append(mhf.model_selection(X_tr_reg_fin, X_ts_reg_fin, y_tr_reg_fin, y_ts_reg_fin, reg_final, 'reg'))\n",
    "print(\"---\\nR-Squared: \", rsq[0][0],\" ---\\nRMSE:\", rsq[0][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Models Playground/SandBox:\n",
    "#### Try different combinations of models, alternative data features etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XTrain, yTrain shapes BEFORE Oversampling: (6710, 725) and (6710, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:75: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XTrain, yTrain shapes AFTER Oversampling: (11858, 725) and (11858,)\n",
      "Shape of XTrain: (11858, 725) yTrain: (3614, 725) XTest: (11858, 1) yTest: (3614, 1)\n",
      "\n",
      "----\n",
      "\n",
      "Classifier has no feature importance attributes\n",
      "Now getting train test splits for regression...\n",
      "Shapes:\n",
      " (981, 725)\n",
      ",(205, 725)\n",
      ",(981,)\n",
      ",(205,)\n",
      "---\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.94      0.95      0.95      3209\n",
      "          1       0.58      0.51      0.54       405\n",
      "\n",
      "avg / total       0.90      0.90      0.90      3614\n",
      " ---\n",
      " Confusion Matrix:\n",
      " [[3061  148]\n",
      " [ 200  205]] ---\n",
      "       RSquared: 0.8485071100273488  and RMSE: 12.395202997532936\n"
     ]
    }
   ],
   "source": [
    "clf_play = MLPClassifier(hidden_layer_sizes=(100,30, 100), activation='relu', solver='adam', alpha=0.0001\n",
    "                         , batch_size='auto', learning_rate='constant', learning_rate_init=0.001\n",
    "                         , power_t=0.5, max_iter=700, shuffle=True, random_state=121, tol=0.0001\n",
    "                         , verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True\n",
    "                         , early_stopping=True, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
    "reg_play = ExtraTreesRegressor(n_estimators=900 ,max_features= 50,max_depth= 50, random_state=121)\n",
    "\n",
    "df_pred_play, df_true_pos_play,  d_feat_imp_clf_play, clfreport_play, cmatrix_play, r2_play, rmse_play = mhf.run_combined_classify_regress_model_prefit(\n",
    "                                            data, ddate, delayed,clf_play, reg_play,test_size=0.35, use_smote=True, show_plot=False)\n",
    "print(\"---\\nClassification Report:\\n {} ---\\n Confusion Matrix:\\n {} ---\\n \\\n",
    "      RSquared: {}  and RMSE: {}\".format(clfreport_play, cmatrix_play, r2_play, rmse_play))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Rand\n",
    "# {'f1': {'delayed': 0.45378151260504201, 'on-time': 0.95100256294286156},\n",
    "#  'precision': {'delayed': 0.71052631578947367, 'on-time': 0.92114485981308414},\n",
    "#  'recall': {'delayed': 0.33333333333333331, 'on-time': 0.98286070426924277}}\n",
    "# R-Squared:  0.807007307896 RMSE : 15.913437136 Target Variable Mean:  26.844444444444445\n",
    "# 135 delays detected\n",
    "\n",
    "#Xtra\n",
    "# {'f1': {'delayed': 0.60205580029368577, 'on-time': 0.95860699557049023},\n",
    "#  'precision': {'delayed': 0.74275362318840576, 'on-time': 0.94008388256440978},\n",
    "#  'recall': {'delayed': 0.50617283950617287, 'on-time': 0.97787472732938607}}\n",
    "    \n",
    "# R-Squared:  0.862636593432  ---\n",
    "# RMSE: 11.9738387556\n",
    "# 205 delays detected same(test-set size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checkpoint 3\n",
    "### Next Notebook: Final Model and Results"
   ]
  }
 ],
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